code: def get_gpt2_flash_attention_forward():
      from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
  
      from colossalai.kernel.cuda_native import (
          AttnMaskType,
          ColoAttention,
      )
  
      def split_heads(tensor, num_heads, attn_head_size):
          """
          Splits hidden_size dim into attn_head_size and num_heads
          """
          new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
          tensor = tensor.view(new_shape)
          return tensor
  
      def forward(
          self: GPT2Attention,
          hidden_states: Optional[Tuple[torch.FloatTensor]],
          layer_past: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.FloatTensor] = None,
          head_mask: Optional[torch.FloatTensor] = None,
          encoder_hidden_states: Optional[torch.Tensor] = None,
          encoder_attention_mask: Optional[torch.FloatTensor] = None,
          use_cache: Optional[bool] = False,
          output_attentions: Optional[bool] = False,
      ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
          if encoder_hidden_states is not None:
              if not hasattr(self, "q_attn"):
                  raise ValueError(
                      "If class is used as cross attention, the weights `q_attn` have to be defined. "
                      "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
                  )
  
              query = self.q_attn(hidden_states)
              key, value = self.c_attn(encoder_hidden_states).split(
                  self.split_size, dim=2
              )
              attention_mask = encoder_attention_mask
          else:
              query, key, value = self.c_attn(hidden_states).split(
                  self.split_size, dim=2
              )
  
          query = split_heads(query, self.num_heads, self.head_dim)
          key = split_heads(key, self.num_heads, self.head_dim)
          value = split_heads(value, self.num_heads, self.head_dim)
  
          if layer_past is not None:
              past_key, past_value = layer_past
              key = torch.cat((past_key, key), dim=1)
              value = torch.cat((past_value, value), dim=1)
  
          if use_cache is True:
              present = (key, value)
          else:
              present = None
  
          if not self.is_cross_attention:
              attn_mask_type = AttnMaskType.causal
              flash_attention_mask = None
          if attention_mask != None:
              flash_attention_mask = ~(
                  attention_mask[:, :, -1].squeeze(1).to(torch.bool)
              ).contiguous()
              if not torch.all(flash_attention_mask):
                  if attn_mask_type == AttnMaskType.causal:
                      attn_mask_type == AttnMaskType.paddedcausal
                  else:
                      attn_mask_type = AttnMaskType.padding
  
          scale = value.size(-1) ** -0.5
          if self.scale_attn_by_inverse_layer_idx:
              scale = scale * (1 / float(self.layer_idx + 1))
  
          # use coloattention
          attention = ColoAttention(
              embed_dim=self.embed_dim,
              num_heads=self.num_heads,
              dropout=self.attn_dropout.p,
              scale=scale,
          )
  
          attn_output = attention(
              query,
              key,
              value,
              attn_mask=flash_attention_mask,
              attn_mask_type=attn_mask_type,
          )
  
          attn_output = self.c_proj(attn_output)
          attn_output = self.resid_dropout(attn_output)
          outputs = (attn_output, present, None)
  
          return outputs
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/gpt2.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel, GPT2DoubleHeadsModelOutput, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
  from colossalai.shardformer.shard import ShardConfig
label_desc: Use image node_id 1 for calling a global function with example usage: get_gpt2_flash_attention_forward() and returns: forward, tensor, outputs